chat-deepseek
GitHub调用 DeepSeek v4-flash-0731 模型进行代码阅读、编写、重构及多步自动化规划。适用于需要长上下文和 Agent 工作流的开发任务，性价比高。
触发场景
安装
npx skills add zerogpu/zerogpu-router --skill chat-deepseek -g -y
SKILL.md
Frontmatter
{
"name": "chat-deepseek",
"description": "Chat with deepseek-v4-flash-0731, a 284B MoE model (13B active per token) with a 1M-token context window, tuned for coding and agentic workflows. Use for reading or writing code across a large codebase, porting and refactoring, or planning multi-step automation. Cheaper than chat-glm, with four times its context.",
"allowed-tools": "Bash(zerogpu chat_completions *)",
"argument-hint": "<text>"
}
Call deepseek-v4-flash-0731. $ARGUMENTS is the raw prompt — pass it verbatim, no escaping or quoting required (the heredoc below handles every shell metacharacter, newline, quote, and paren safely):
zerogpu chat_completions -m deepseek-v4-flash-0731 <<'ZGPU_END_OF_INPUT'
$ARGUMENTS
ZGPU_END_OF_INPUT
At $0.16 / $0.38 per 1M input/output tokens this is the cheaper of the two 1M-context models on the platform — roughly a seventh of /zerogpu-router:chat-glm on input and a ninth on output. Prefer it whenever the task is code or tool-use rather than sheer input size. For a prompt that fits in 131K tokens, /zerogpu-router:chat costs about the same on input and over half again as much on output, so choose on task fit rather than price.
Output is the assistant's answer as plain text — the model's reasoning trace comes back in a separate field and is not printed. Relay the answer as-is — do not rewrite or expand it.
Savings note: only if the command output literally contains a line starting with 💰 ZeroGPU savings, append that exact line, unchanged, as the last line of your reply. If no such line is present, say nothing about savings and do not mention or suggest /zerogpu-router:cost-savings — this note is intentionally occasional, not shown every time.
版本历史
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4e1b070
当前 2026-09-22 08:06
模型升级至 deepseek-v4-flash-0731；API 从 Responses 切换为 Chat Completions；价格调整并更新对比描述。
- cee9321 2026-08-02 21:14
